A Reconfigurable Ferroelectric Transistor as An Ultra‐Scaled Cell for Low‐Power In‐Memory Data Processing

Abstract Compact in‐memory computing architectures are desirable to embed artificial intelligence (AI) in resource‐restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field‐effect transistor (ferro‐TFET) is presented that can be used as an ultra‐scaled cell for low‐power in‐memory data processing. A gate‐all‐around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non‐volatilely reconfigurable anti‐ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single‐gate ferro‐TFET. It is shown that the ferro‐TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse‐response method considering the effect of device‐to‐device variation based on statistics. Benefiting from the miniaturized footprint, non‐volatility, and low‐power operation, ferro‐TFETs show promises as a one‐transistor in‐memory computing cell for area‐ and energy‐efficient edge AI applications.